Automate your Data Mesh implementation

Data Mesh puts the responsibility for implementation back on the data domain teams. Datavault Builder helps these data domain teams rapidly deliver data products to their customers in an understandable and maintainable manner.

As Datavault Builder is multi-tenant capable, every data domain team can work in their independent environment on their release schedule.

Business Model Driven

By starting with the business model, we can involve the data product consumers from the beginning and make the output after implementation comprehensible to them.

Integrating different data sources makes the data products rich in content and deliver business value.

Covering the full processing chain

By implementing full data integration, historization, and harmonization of data, all your documentation and governance are managed by the Datavault Builder.

Automatic Data Lineage

A critical part of Data Mesh implementations is self-generated documentation which is always up-to-date. Datavault Builder derives all of this from the generated code and ensures that everything is in sync – always.

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How to automate Data Mesh

See this live demo how to create data products in two different data domains and how to combine them.

About Data Mesh

Data mesh is a data governance framework that aims to optimize data usage within an organization by establishing a shared understanding of data among all stakeholders and promoting decentralized ownership and control of data assets. It promotes the creation of a “mesh” of data products, each owned and governed by a cross-functional team that is responsible for the end-to-end lifecycle of the data.

The goal of data mesh is to create a decentralized data governance model that allows teams to independently discover, understand, and use data without having to rely on centralized data management teams or processes. This can help organizations to become more agile and responsive to changing business needs, as well as to better leverage their data assets for competitive advantage.

Some key principles of data mesh include:

Decentralized ownership and control of data: Teams are responsible for the end-to-end lifecycle of the data they use, including its discovery, definition, quality, access, and use.
Shared understanding of data: Teams work to create a shared understanding of data across the organization through the use of common data definitions and vocabularies.
Data as a product: Data is treated as a product that is owned and governed by a team, rather than as a shared resource managed by a centralized data management group.
Continuous improvement: Teams are encouraged to continuously improve the quality and value of their data products through the use of feedback loops and agile processes.
By following these principles, data mesh can help organizations to better leverage their data assets, improve data quality and trust, and increase agility and responsiveness to changing business needs.

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